CUED Publications database

Mining customer-related data to enhance home delivery in ecommerce: An experimental study

Pan, S and Han, Y and Qiao, B and Grover-Silva, E and Giannikas, V (2016) Mining customer-related data to enhance home delivery in ecommerce: An experimental study. In: UNSPECIFIED.

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Abstract

In a B2C e-commerce environment, home delivery service refers to delivering goods from an e-retailer's storage point to a customer's home. High rate of failed delivery due to the customer's absence causes significant loss of logistics efficiency. This paper aims to study innovative solutions to the problem, such as data-related techniques. This paper proposes a methodological approach to use customer-related data to optimize home delivery. The idea is to estimate the attendance probability of a customer via mining his electricity consumption data, in order to improve the success rate of delivery and optimize transportation. Computational experiments reveal that the proposed approach could reduce the total distance from 3% to 20%, and theoretically increase the success rate around 18%-26%. Being an experimental study, this paper demonstrates the effectiveness of data-related techniques or data-based solutions in home delivery problem, and provides a methodological approach to this line of research.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Subjects: UNSPECIFIED
Divisions: Div E > Manufacturing Systems
Div E > Production Processes
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:34
Last Modified: 26 Sep 2017 01:45
DOI: